Magnetic resonance (MR) imaging is routinely used to diagnose prostate cancer (PCa) and identify the stage of PCa. PCa may induce changes in the shape of the prostate capsule and central gland (CG) in biopsy positive (Bx+) patients relative to biopsy negative (Bx−) patients, elevated-prostate specific antigen (PSA) patients, or normal patients. PCa may also induce changes in the volume of the prostate and CG in Bx+ patients relative to Bx−, elevated-PSA, and normal patients. These changes in the shape and volume of the prostate may be observed in T2 weighted (T2w) MRI images.
Radiation therapy is a common treatment for PCa. However, radiation therapy has been reported to have failure rates as high as 25%. Predicting biochemical recurrence (BcR) prior to radiation therapy may enable better planning and personalization of treatment. MR images may be used to assist the prediction of BcR in PCa patients. However, when obvious extra-capsular spread of the disease is not present, conventional approaches employing MRI are not useful for distinguishing patients who will experience BcR from those who will not.
Multi-parametric MRI is widely used in the management of PCa to improve the localization and local staging of the disease. Despite its broad adoption in the management of PCa, conventional approaches using MRI may suffer from a large variability in MRI acquisition parameters and reporting. This large variability may occur both within an individual institution (e.g., hospital, university) and across multiple institutions. Conventional approaches to MRI-based PCa diagnosis and identification may employ protocols or guidelines for imaging acquisition parameters and findings reporting, although score interpretation and detection thresholds, particularly across multiple institutions, have not been uniformly applied or exhaustively studied. Furthermore, implementing protocols and guidelines across different institutions takes time, costs money, and puts a patient at additional risk if the guidelines and protocols are not consistently applied.
One conventional approach to reduce the subjectivity of image appearance assessment and score assessment includes employing computer aided detection and diagnosis (CADx) techniques. Conventional CADx approaches typically employ image-driven textures acquired from multiple MRI protocols, and may combine the image-driven textures with pharmacokinetic behavior quantifiers and machine learning techniques. However, these conventional approaches have not been generalized across different scanners or across different institutions.
Conventional MRI protocols may also lack tissue-specific numerical meaning. A lack of tissue-specific numerical meaning may result in inconsistent MRI intensities, even for the same patient, the same scanned region, or the same scanner. The impact of inconsistent MRI intensities within the same patient, same scanned region, or same scanner may be exacerbated across multiple institutions. Thus, conventional approaches to PCa diagnosis and management using MRI are less than optimally accurate or efficient, and may not optimally utilize information gathered across different populations by different institutions.